R Notebook

We will take a look at ggspavis and the SpatialExperiment object in Bioconductor.

library(zellkonverter)
library(SpatialExperiment)
library(ggspavis)
library(stardust)
library(RColorBrewer)

Let’s read it and convert it with zellkonverter

path_adata <- "/Users/giovanni.palla/Datasets/advomics_data_spatial"
ad <- readH5AD(paste0(path_adata,"/V10F24-105_A1_stereoscope.h5ad"))

Warning: The names of these selected uns$spatial items have been modified to match R conventions: 'V10F24-105_A1' ->
'V10F24.105_A1'Warning: The names of these selected uns items have been modified to match R conventions: '_scvi_manager_uuid' ->
'X_scvi_manager_uuid' and '_scvi_uuid' -> 'X_scvi_uuid'Warning: The names of these selected obs columns have been modified to match R conventions: '_indices' -> 'X_indices',
'ELF3_AGBL2 positive cells' -> 'ELF3_AGBL2.positive.cells', 'Endocardial cells' -> 'Endocardial.cells', 'Epicardial
fat cells' -> 'Epicardial.fat.cells', 'Lymphatic endothelial cells' -> 'Lymphatic.endothelial.cells', 'Lymphoid
cells' -> 'Lymphoid.cells', 'Myeloid cells' -> 'Myeloid.cells', 'SATB2_LRRC7 positive cells' ->
'SATB2_LRRC7.positive.cells', 'Schwann cells' -> 'Schwann.cells', 'Smooth muscle cells' -> 'Smooth.muscle.cells',
'Stromal cells' -> 'Stromal.cells', 'Vascular endothelial cells' -> 'Vascular.endothelial.cells', and 'Visceral
neurons' -> 'Visceral.neurons'

Look at the SpatialExperimentObject

coords <- as.matrix(reducedDim(ad, "spatial"))
colnames(coords) = c("x","y")
spe <- SpatialExperiment(
    assay = assay(ad,"X"), 
    colData = ad@colData, 
    spatialCoords = coords,
)
spe[["sample_id"]] <- "V10F24-105_A1"
spe

class: SpatialExperiment 
dim: 502 2169 
metadata(0):
assays(1): ''
rownames(502): A2M ABCA9 ... ZNF385D ZNF468
rowData names(0):
colnames(2169): AAACAATCTACTAGCA-1 AAACAGAGCGACTCCT-1 ... TTGTTTCACATCCAGG-1 TTGTTTGTGTAAATTC-1
colData names(20): in_tissue array_row ... Visceral.neurons sample_id
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):
spatialCoords names(2) : x y
imgData names(1): sample_id

path_img <- "./V10F24-105_A1.png"
spe <- addImg(spe, 
    sample_id = "V10F24-105_A1", 
    image_id = "V10F24-105_A1",
    imageSource = path_img, 
    scaleFactor = 0.04155585, 
    load = TRUE)

colData(spe)

DataFrame with 2169 rows and 20 columns
                   in_tissue array_row array_col   leiden X_indices Cardiomyocytes ELF3_AGBL2.positive.cells
                   <numeric> <numeric> <numeric> <factor> <numeric>      <numeric>                 <numeric>
AAACAATCTACTAGCA-1         1         3        43        4         0    1.73613e-06               2.63602e-06
AAACAGAGCGACTCCT-1         1        14        94        5         1    4.65527e-01               1.11768e-04
AAACCGGGTAGGTACC-1         1        42        28        8         2    5.07666e-01               9.17303e-02
AAACCGTTCGTCCAGG-1         1        52        42        0         3    1.29556e-02               4.75903e-02
AAACCTAAGCAGCCGG-1         1        65        83        4         4    1.22499e-06               3.30878e-06
...                      ...       ...       ...      ...       ...            ...                       ...
TTGTGTATGCCACCAA-1         1        56        60        3      2164    2.21985e-01               1.20458e-05
TTGTGTTTCCCGAAAG-1         1        51        59        3      2165    7.42314e-02               1.36711e-02
TTGTTCAGTGTGCTAC-1         1        24        64        1      2166    2.02858e-01               8.77654e-06
TTGTTTCACATCCAGG-1         1        58        42        0      2167    5.49917e-02               1.71335e-04
TTGTTTGTGTAAATTC-1         1         7        51        4      2168    3.68276e-05               6.57713e-06
                   Endocardial.cells Epicardial.fat.cells Lymphatic.endothelial.cells Lymphoid.cells Megakaryocytes
                           <numeric>            <numeric>                   <numeric>      <numeric>      <numeric>
AAACAATCTACTAGCA-1       7.18265e-06          4.70958e-02                  0.07174900    3.94957e-06    1.85054e-06
AAACAGAGCGACTCCT-1       1.35986e-01          3.82232e-02                  0.00130384    1.25679e-01    4.31108e-05
AAACCGGGTAGGTACC-1       1.70480e-01          3.03219e-02                  0.06204053    2.04559e-04    1.33041e-02
AAACCGTTCGTCCAGG-1       4.47221e-06          2.72076e-06                  0.01039361    2.12998e-01    1.66399e-06
AAACCTAAGCAGCCGG-1       2.83412e-06          3.03445e-02                  0.06675252    3.53437e-06    6.76978e-06
...                              ...                  ...                         ...            ...            ...
TTGTGTATGCCACCAA-1       1.73486e-01          7.52264e-02                 3.45799e-02    1.23511e-01    2.97905e-02
TTGTGTTTCCCGAAAG-1       2.29955e-01          7.56451e-06                 1.04336e-05    1.16271e-01    4.90569e-06
TTGTTCAGTGTGCTAC-1       7.66914e-05          4.79027e-06                 1.54969e-02    1.82428e-01    6.98415e-06
TTGTTTCACATCCAGG-1       1.06166e-05          1.54471e-02                 2.11418e-02    2.89142e-01    1.98310e-02
TTGTTTGTGTAAATTC-1       1.86435e-01          2.11039e-02                 4.57555e-06    6.86577e-06    3.71163e-02
                   Myeloid.cells SATB2_LRRC7.positive.cells Schwann.cells Smooth.muscle.cells Stromal.cells
                       <numeric>                  <numeric>     <numeric>           <numeric>     <numeric>
AAACAATCTACTAGCA-1   5.98324e-02                3.90242e-02   5.10024e-02           0.1125237   6.09225e-01
AAACAGAGCGACTCCT-1   1.38215e-02                4.57504e-02   1.05696e-01           0.0230188   1.05394e-05
AAACCGGGTAGGTACC-1   5.26261e-05                3.70224e-05   9.18031e-06           0.0192380   7.65207e-02
AAACCGTTCGTCCAGG-1   2.19633e-02                1.89110e-01   6.00117e-03           0.0444164   1.23533e-01
AAACCTAAGCAGCCGG-1   7.37372e-02                6.39107e-06   2.43021e-02           0.3608190   2.32163e-01
...                          ...                        ...           ...                 ...           ...
TTGTGTATGCCACCAA-1   4.02957e-02                  0.0855837   8.27646e-06         2.00553e-02     0.1954228
TTGTGTTTCCCGAAAG-1   3.29083e-04                  0.2335415   4.53491e-02         1.56497e-01     0.0928041
TTGTTCAGTGTGCTAC-1   1.13313e-01                  0.0754649   8.12851e-06         7.34828e-06     0.2807275
TTGTTTCACATCCAGG-1   6.06932e-06                  0.0422673   3.69965e-02         6.17583e-03     0.2398590
TTGTTTGTGTAAATTC-1   3.44671e-02                  0.0748089   2.32609e-02         1.64804e-01     0.2895845
                   Vascular.endothelial.cells Visceral.neurons     sample_id
                                    <numeric>        <numeric>   <character>
AAACAATCTACTAGCA-1                2.41417e-06       0.00952812 V10F24-105_A1
AAACAGAGCGACTCCT-1                1.64129e-05       0.04481268 V10F24-105_A1
AAACCGGGTAGGTACC-1                5.69946e-03       0.02269486 V10F24-105_A1
AAACCGTTCGTCCAGG-1                1.64334e-01       0.16669481 V10F24-105_A1
AAACCTAAGCAGCCGG-1                1.24272e-01       0.08758564 V10F24-105_A1
...                                       ...              ...           ...
TTGTGTATGCCACCAA-1                7.74797e-06      3.61712e-05 V10F24-105_A1
TTGTGTTTCCCGAAAG-1                3.73034e-02      2.40056e-05 V10F24-105_A1
TTGTTCAGTGTGCTAC-1                1.02289e-01      2.73095e-02 V10F24-105_A1
TTGTTTCACATCCAGG-1                1.55952e-01      1.18008e-01 V10F24-105_A1
TTGTTTGTGTAAATTC-1                1.27973e-01      4.03921e-02 V10F24-105_A1

plotSpots(spe,annotate = "Stromal.cells")

plotVisium(spe,fill = "array_row")

plotVisium(spe,fill = "Stromal.cells",)

countMatrix = as.data.frame(spe@assays@data@listData)
spotPositions = as.data.frame(spatialCoords(spe))

output <- weightStardust(
  countMatrix = countMatrix, spotPositions = spotPositions, 
                         spaceWeight = 0.75, pcaDimensions=10, res=0.8)

Computing nearest neighbor graph
Computing SNN

clusters_identities <- output@active.ident

Plot cluster identities

spe@colData$stardust_clusters <- clusters_identities
getPalette = colorRampPalette(brewer.pal(12, "Set1"))

Warning: n too large, allowed maximum for palette Set1 is 9
Returning the palette you asked for with that many colors

plotSpots(spe,annotate = "stardust_clusters",palette = getPalette(12), size=2)